
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "88"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 86 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 82 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	9.670192	8.562520	71.297783	73.432390	21.101946	26.770263	26.046108	28.255915	0.0561	0.0632	0.0030	1.379413	1.558586
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	273.305329	273.371680	inf	inf	58.838280	65.987522	330.917549	345.857342	nan	nan	nan	0.000000	0.000000
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.492904	1.441610	2.289541	2.163806	0.781452	0.843487	-0.947928	0.398568	0.0357	0.0420	0.0087	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.891253	21.159800	86.008176	88.839232	26.339543	32.084583	3.120407	1.843920	0.0496	0.0525	0.0002	1.343790	1.466609
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.054294	23.649600	80.060426	82.957799	27.806716	33.995212	4.846353	3.324740	0.0536	0.0563	0.0005	1.406194	1.536788
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.117469	21.820008	74.519423	76.963991	16.686889	21.583109	18.789003	16.379902	0.0559	0.0617	0.0023	1.314951	1.400609
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	214.346218	215.166333	inf	inf	22.278655	25.532570	22.140691	20.276188	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	21.181857	22.505477	120.822075	125.343812	5.709886	7.316076	9.566389	9.437793	nan	nan	nan	1.757096	2.976680
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.015335	19.509966	28.710758	29.250106	9.582988	11.150919	7.224505	5.476819	0.0523	0.0573	0.0016	1.281521	1.286498
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	163.040517	162.713891	inf	inf	15.123845	16.081776	942.442711	1473.680643	nan	nan	nan	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.435762	30.676662	97.268900	100.519426	15.351054	17.546765	12.960424	11.741946	0.0470	0.0487	-0.0002	1.550270	1.982406
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.124902	21.068419	30.481916	31.430630	19.178334	22.733341	0.721790	-0.065966	0.0475	0.0480	-0.0000	0.000000	0.000000
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.753236	18.026484	27.371626	28.541885	23.047851	27.502987	0.875109	0.021616	0.0447	0.0451	-0.0004	1.177336	1.179294
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.957698	24.881414	31.773500	32.154634	18.155310	22.189109	1.223268	0.052582	0.0495	0.0500	-0.0003	1.225424	1.220597
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.388290	18.761862	31.849346	32.956160	20.067100	23.285890	0.717525	-0.119031	0.0513	0.0524	0.0002	1.116120	1.112813
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.395152	18.778582	36.078611	37.479944	22.537517	26.484665	2.586212	0.986720	0.0472	0.0492	0.0000	1.327662	1.338308
2459578	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.411657	23.941739	29.488342	30.077777	20.308510	24.182199	1.068033	0.040768	0.0476	0.0492	-0.0002	1.229527	1.235728
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.282100	18.348136	23.474371	24.193333	18.677273	21.520042	1.925753	0.627899	0.0478	0.0497	-0.0002	1.312644	1.345302
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.229518	19.508773	22.487454	24.032261	15.140275	16.665242	7.261439	6.126509	0.0551	0.0560	-0.0004	1.327407	1.348723
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.367026	17.389966	22.291404	22.957920	20.397210	23.955302	1.784779	0.421438	0.0486	0.0500	0.0000	1.263742	1.277103
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.205599	18.164200	38.365645	39.453877	23.985890	27.563254	2.060572	0.913308	0.0478	0.0501	0.0004	1.330206	1.345743
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.992779	18.091195	22.388614	23.089655	15.466054	17.473189	4.184022	1.721121	0.0481	0.0499	-0.0003	1.218977	1.221422
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.436833	1.125553	0.130146	0.005501	0.686760	-0.604626	3.601741	-0.024118	0.0330	0.0415	0.0043	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.998576	17.644135	29.267128	29.925282	18.518038	21.255935	0.300716	-0.419465	0.0356	0.0369	0.0001	1.184846	1.183277
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	4299.241007	4299.241007	4031.441662	4031.441662	-0.749395	-0.749395	-1.917447	-1.917447	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.242484	15.504017	31.099737	31.922210	2.206776	2.188105	1.693543	0.709126	0.0478	0.0512	0.0013	1.191483	1.200170
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.272836	17.262921	59.466196	60.901361	19.098168	21.830153	3.595314	1.653553	0.0488	0.0530	0.0021	1.244747	1.245162
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.013203	11.329821	15.266547	15.677707	2.361133	2.628531	1.166562	0.451941	0.0466	0.0487	0.0002	1.206768	1.228425
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.796117	12.286040	14.524969	14.910001	2.645200	2.822154	2.119729	0.625985	0.0506	0.0526	-0.0002	1.233730	1.237565
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.806089	11.113928	14.938075	15.120309	2.264506	2.774041	1.327465	0.238093	0.0479	0.0514	0.0005	1.239153	1.259931
2459559	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.805846	14.401619	16.308945	16.615084	2.541529	3.033311	1.689153	0.531069	0.0499	0.0527	0.0004	1.202193	1.206191
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.790221	18.813897	30.498429	31.715753	20.938731	22.809171	2.388045	0.825713	0.0464	0.0487	-0.0004	1.267848	1.296231
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0320	0.0372	0.0028	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.028699	19.234385	32.971933	34.369770	18.873566	20.514725	4.781796	2.096137	0.0504	0.0540	0.0010	1.262334	1.296306
2459554	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0490	0.0518	0.0004	nan	nan
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0541	0.0568	0.0004	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.024785	22.769490	44.532834	45.271844	28.907246	31.827640	4.031931	2.533590	0.0468	0.0492	-0.0001	1.455090	1.541156
2459551	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0482	0.0484	0.0008	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.481579	16.903740	33.438367	34.163315	20.363988	22.373478	2.152651	0.546751	0.0451	0.0473	-0.0001	1.274533	1.289557
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.050766	23.294966	68.286916	69.625448	1.097848	1.911769	2.793947	1.127510	0.0518	0.0594	0.0051	1.258947	1.302294
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.647117	27.927334	57.876869	58.818151	29.144263	31.331806	3.925019	1.046340	0.0438	0.0449	0.0000	1.291518	1.308023
2459535	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.346373	13.762444	14.129231	14.525714	2.679987	5.913008	0.759524	0.044216	0.0503	0.0479	0.0027	1.229926	1.232923
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	13.216031	14.125703	13.130250	13.425009	2.508365	4.187887	0.596791	-0.239905	0.0418	0.0411	0.0012	1.255986	1.241800
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.147491	20.938508	31.141939	32.057981	17.557112	20.455649	2.386235	0.842666	0.0420	0.0422	-0.0006	1.257506	1.244160
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.138777	24.063561	42.013486	43.536448	21.933487	25.337455	1.620146	0.390343	0.0424	0.0425	-0.0008	1.271344	1.258591
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.254626	12.014316	13.295622	13.600633	3.298117	4.154800	1.253027	0.152817	0.0556	0.0525	0.0034	1.236217	1.240283
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.356336	28.579058	47.878383	49.181335	41.144500	35.649780	0.581801	0.191933	0.0560	0.0923	0.0196	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	25.447378	23.955078	45.639228	45.949806	18.108171	30.013134	2.260098	0.728917	nan	nan	nan	1.615936	1.613895
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	19.886355	24.522712	45.160128	47.074033	11.095977	23.661958	2.431630	0.886474	0.0538	0.0592	0.0014	1.254460	1.266376
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.803972	1.600289	0.163004	0.157580	0.443103	-0.149883	2.937800	0.115738	0.0378	0.0378	0.0022	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.794799	31.692138	44.951463	46.612765	18.364601	15.811032	12.900797	14.807090	0.0480	0.0549	0.0025	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.983172	31.676827	41.360475	44.465684	7.929411	10.132954	3.624773	2.451089	0.0504	0.0512	-0.0005	1.259395	1.313709
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.143008	31.284853	74.489062	76.329184	33.957119	33.513577	3.296289	2.466333	0.0404	0.0375	0.0026	0.000000	0.000000
2459501	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.681904	25.068510	49.701419	51.123906	28.664159	27.870163	2.446466	0.583499	0.0467	0.0492	0.0001	0.000000	0.000000
2459500	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.904420	27.930969	61.655678	63.045899	27.308390	27.381044	1.190821	0.755189	0.0480	0.0530	-0.0009	0.000000	0.000000
2459499	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	26.802507	30.061460	79.322758	81.329879	27.434677	27.703551	4.494814	11.127066	0.0538	0.0680	0.0045	0.000000	0.000000
2459498	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.615560	25.663205	21.887018	72.317745	31.906864	24.586106	301.522280	5.316236	0.0601	0.0850	0.0168	0.000000	0.000000
2459497	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.441223	29.595321	79.996759	81.955150	31.675645	31.875237	3.330277	3.060123	0.0523	0.0570	-0.0010	0.000000	0.000000
2459496	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.105826	30.954172	72.900064	74.429774	28.169338	28.809816	2.193076	1.790801	0.0506	0.0513	-0.0027	0.000000	0.000000
2459495	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	23.333734	25.006778	68.024836	69.329916	24.208820	25.246437	2.984958	2.349231	0.0489	0.0506	-0.0020	0.000000	0.000000
2459494	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	25.306710	26.864818	72.382775	73.846499	27.211850	28.242331	7.259918	5.871997	0.0449	0.0457	-0.0001	0.000000	0.000000
2459492	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.847580	29.823305	49.510810	50.858964	23.960833	29.059981	7.530806	4.458414	0.0417	0.0385	-0.0021	0.000000	0.000000
2459491	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	31.501315	30.211796	53.733667	55.751940	33.121449	43.734734	4.804811	12.361632	0.0391	0.0402	-0.0046	0.000000	0.000000
2459490	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	37.754129	36.595198	56.334168	57.764376	22.525337	26.802537	6.354835	5.176125	0.0438	0.0425	-0.0011	0.000000	0.000000
2459489	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.551117	29.097792	51.061844	52.583859	22.475068	28.512225	5.160692	7.411904	0.0437	0.0434	-0.0017	0.000000	0.000000
2459488	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.813093	27.768663	42.704550	43.672065	24.676842	30.931895	9.418261	8.829225	0.0439	0.0427	-0.0018	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 88 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 88, Node 9:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 88: 2459610

Antenna 88: 2459609

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Antenna 88: 2459501

Antenna 88: 2459500

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Antenna 88: 2459498

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶